Evaluation of CMIP6 Historical Simulations over IGAD region of Eastern Africa

Author:

Omay Paulino Omoj1,Muthama Nzioka J.1,Oludhe Christopher1,Kinama Josiah M.1,Artan Guleid2,Atheru Zachary2

Affiliation:

1. University of Nairobi

2. IGAD Climate Prediction and Applications Center

Abstract

Abstract Accuracy of model’s simulations are critical for climate change and its socio-economic impact. In this study, we evaluated 23 Global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). The main objective was to identify top 10 best performance models in capturing patterns of rainfall for the 1981–2014 period over the Intergovernmental Authority on Development (IGAD) region of Eastern Africa. The total rainfall, annual cycle, continuous, categorical and Volumatic statistical metrics, scatter plots, Cumulative Distribution Function (CDF) and colored code portrait were used to assess the patterns of total rainfall. Results indicate that most CMIP6 models generally capture the characteristics of the observed climatology pattern of total rainfall, bimodal and unimodal rainfall regimes. The majority of models over Arid and Semi-Arid Lands (ASALs) in Kenya, Somalia, Ethiopia and Sudan scored lowest skills, highest bias and over-estimated rainfall. In addition, 21 out of 23 CMIP6 over-estimated rainfall over most parts of the region. The ACCESS-ESM1-5 and MIROC6 are the most over-estimated models opposed to CNRM-CM6-1HR as the most model under-estimated rainfall, highest bias and RMSE values. The regional and sub-national analysis showed, it is inconclusive to select best performed models based on individual metric. Out of 23 models, the INM-CM5-0, HadGEM3-GC31-MM, CMCC-CM2-HR4, IPSL-CM6A-LR, KACE-1-0-G, EC-Earth3, NorESM2-MM, GFDL-ESM4, TaiESM1 and KIOST-ESM are the best 10 performance models over IGAD region. These findings highlight the importance of selecting best performance models for mapping present and future hotspots and extreme rainfall events over the IGAD region of Eastern Africa.

Publisher

Research Square Platform LLC

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